There is a technology in which an object is recognized from an image of the object captured by an image capturing section. In such a technology, an appearance feature amount of the object is extracted from the image and then compared with feature amount data of each reference image registered in a recognition dictionary file to calculate a similarity degree of the feature amounts. Then, an object equivalent to the reference image having the highest similarity degree is recognized as the object photographed by the image capturing section.
In recent years, it has been proposed to apply such an object recognition technology to a checkout system (POS system) of a retail store to recognize commodities purchased by a customer. In this case, feature amount data indicating the appearance feature amount of a commodity associated with items of each commodity, respectively, and specified with the item is registered in the recognition dictionary file. In other words, the commodity of which the feature amount data is not registered in the recognition dictionary file cannot be recognized.
For example, in a retail store which deals in fresh food, the sales of an unregistered commodity of which the feature amount data is not registered in the recognition dictionary file may be started due to the commodity replacement and the like. In this case, the store adds the feature amount data of the unregistered commodity to the recognition dictionary file before the sales starts. However, the addition of the feature amount data of the unregistered commodity may leads to a reduction in the recognition rate of the commodity which has already been registered. There also exists a case where the recognition rate of the commodity of which the feature amount data is added is lower than the expected recognition rate. Such a problem may also be caused by the change of the feature amount data as well as by the addition of the feature amount data.